Fast Data in the Era of Big Data: Tiwtter s Real-Time Related Query Suggestion Architecture

Transcription

1 Fast Data in the Era of Big Data: Tiwtter s Real-Time Related Query Suggestion Architecture Gilad Mishne, Jeff Dalton, Zhenghua Li, Aneesh Sharma, Jimmy Lin Adeniyi Abdul

2 Agenda Abstract Introduction First Solution Second Solution (Deployed Solution) Conclusion

3 Abstract This presentation is based on the architecture of Twitter s real time related query suggestion and spelling correction service. After significant breaking news events, Twitter aims to provide relevant results within minutes; typically ten minutes. Two systems were built to achieve this target but only one was eventually deployed. First implementation was based on a typical Hadoop-based analytics stack. Second implementation, which was eventually deployed is a custom in-memory processing engine.

4 Introduction Architecture behind Twitter's real-time related query suggestion and spelling correction service is collectively called "search Twitter Though a common concept, the Twitter context introduces a real-time "twist" :after significant breaking news events, twitter aims toprovide relevant search results within minutes. At twitter, search assistance needs to be provided in real time and must dynamically adapt to the rapidly evolving "global conversation". The architecture considers 3 aspects of data volume, velocity, & variety, and it addressed the challenges of real-time data processing in the era of "big data Twitter s search assistance architecture exposed a flaw in the typical usage of Hadoop as a "big data" platform. It is not well suited to lowlatency processing.

5 First Solution: Hadoop The first solution sought to take advantage of Twitter's existing analytics platform -Hadoop Incorporated into its' Hadoop platform are components such as Pig, Hbase, ZooKeeper, and Vertica. First version of search assistance was built using this platform. Hadoop Twitter Data is written to the Hadoop Distributed File System (HDFS) viaa number of realtime and batch processes. Intead of directly writing Hadoop code in Java, analystics at Twitter is performed mostly using Pig. Pig is a high-level dataflow language that compiles into physical plans that are executed on Hadoop. Pig provides concise primitives for expressing operations such as projection, selection, group, join, etc

6 Hadoop Twitter

7 Production Pig analytics jobs are coordinated by a work-flow manager called Oink. Oink schedules recurring jobs at fixed intervals (hourly or daily), and it handles dataflow dependencies between jobs. If job B requires data generated by job A, Oink will schedule A,verify that A has successfully complete, and then schedule job B. Oink also preserves execution traces for audit purposes: when a job began, how long it lasted and whether it was completed successfully. Oink schedules hundreds of Pig scripts on a daily basis, which translates into thousands of Hadoop jobs The first version of search assistance was written in Pig, with custom Java UDFs for computation that could not be expressed with Pig primitives.

8 A pig script that aggregates user search sessions, computes termand co-occurrence statistics and ranks related queries and spellings suggestions would run on Hadoop stack. Although the system worked reasonably in terms of output, however, latency was estimated in hours. This is a far cry from the targeted 10 minutes. The latency is primarily attributed to: Data import pipeline moving data from tens of thousands of production hosts onto HDFS. MapReduce jobs

9 Second Solution: In-memory processing engine The Hadoop based system was replaced due to it s inability to meet the latency requirements of the search assistance application. Overall Search Architecture

10 Overall search architecture consists of : Blender : The frontend part which brokers access to all service requests to Twitter s family of search services. Blender has a complete record of users search sessions -thus there is no need for client event Scribe logs. However, it does not have access to clickthrough data. EarlyBird is Twitter s inverted indexing engine. A fleet of these servers ingests tweets from the firehose -a streaming API providing access to all tweets as they are published-to update in-memory indexes. The in-memory processing engine focused on two sources of data tweet fire hose and the Blender query hose (tweets and search sessions ), augmentedby offline processing components.

11 Architecture of the Search Assistance Engine

12 Lightweight in-memory caches periodically read fresh results from HDFS; serve as frontend nodes (operated as Thrift service). Together, the frontend nodes form a single replicated, fault-tolerance service endpoint that can be arbitrarily scaled out to handle increased query load. Request routing to the replicas is handled by ServerSet a Twitter abstraction which provides client-side load-balanced access to replicated service coordinated by the ZooKeeper. Actual computations are performed on backend nodes. The backend processing is replicated for fault tolerance but not shared. Every 5 minutes, computed results are persisted to HDFS the instances perform leader election using ZooKeeper, and the winner proceeds to write its result. Every minute, the frontend caches poll a known HDFS location forupdated results, thus ensuring freshness of query suggestion and spelling corrections.

13 Each backend instance is a multi-threaded application that consists of three major components: The Stats Collector which reads the firehose and query hose. In-memory Stores which hold the most up-to-date statistics. Rankers which periodically execute one or more ranking algorithm by consulting the in-memory stores for the raw features. There are three separate in-memory stores to keep track of relevant statistics: The Session Store keeps track of user sessions observed in the query hose, and not for each, the history of the queries issues in a linked list. The Query Statistics Store retains up-to-date statistics about individual queries. The Query Coocurrence Statistics Store similar to Query Statistic Store, except that it holds data about pairs of cooccuring queries.

14 Rationale for the Search Assistance Engine Architecture The architecture adopted decoupled frontend and backend design due to different scalability requirements. The frontend caches need to scale out with increased query load,whereas the backend face no pressure. Persisting data to HDFS ensures that upon a cold restart, the frontend caches can serve the most recently written results immediately without waiting for the backend. Persisted result can also be analyzed retrospectively with Pig to understand how the service is being used. Since the data is on HDFS, it is easy to join them with log datafor behavior analysis and user data to slice-and-dice by demographic characteristics.

15 Conclusion Obviously, there is a growing recognition that volume, velocity,and variety require different models of computation and alternative processing platforms. An important future direction in data management is bridging thegap between platforms for big data.